Abstract
The vehicle networks on the urban roads have
many important factors that influence the
performance, such as street layouts and intersections
with traffic signs, or inter-vehicle interactions. Thus it
is important to use a realistic mobility model. We use
real world road layouts in TIGER and compared the
performance of the GPSR (position based routing) and
the OLSR (reactive routing) popular routing protocol.
In this paper, we suggest a position-based routing
scheme designed for communication with control node
at intersection area, like light controller, in urban
environment.

1. Introduction
Position-based routing, as it is used by protocols
like Greedy Perimeter Stateless Routing (GPSR) [3], is
very well suited for high dynamic environments such
as inter-vehicle communication on highways. However,
it has been discussed that radio obstacles (like
building), as they are found in urban areas, have a
significant negative impact on the performance of
position based routing.
This paper analyzed the problem of efficiently data
delivery in vehicular ad hoc networks at Urban
environments, specially many intersection deployed
areas like grid. And examines the possibility of
deploying an adaptive control system which collect
vehicle information (position, id, neighbor node) at
intersections, and system that can base its Data
forwarding decision on information coming from
other cars. We assume each vehicle is equipped with a
short-range wireless communication device, as is a

1
2

controller node placed in every intersection with Data
Forwarding System.
The remainder of this paper is organized as follows.
In section 2, we present related work in the field of
Position-based routing in VANET, relevant to our
work. In section 3, we present the simulation
framework we have configured, in order to evaluate
the routing protocol more realistically. Section 4
describes our Intersection Area Data Forwarding
System. And we conclude in section 6.

2. Related work
Vehicular ad hoc networks have been envisioned to
be useful in road safety and many commercial
applications. For example, a vehicular network can be
used to alert drivers to potential traffic jams, providing
increased convenience and efficiency. It can also be
used to propagate emergency warning to drivers
behind a vehicle (or incident) to avoid multi-car
collisions [1].
The Greedy Perimeter Stateless Routing (GPSR)
[3] algorithm belongs to the category of position-based
routing, where an intermediate node forwards a packet
to an immediate neighbor which is geographically
closer to the destination node. This approach is called
greedy forwarding. For that matter, each node needs to
be aware of its own position, the position of its
neighbors as well as the position of the destination
node. However, GPSR is very well suited for highly
dynamic environments such as inter-vehicle
communication on highways. It has been discussed
that radio obstacles, as they are found in urban areas,
have a significant negative impact on the performance
of position based routing

This research was supported by Seoul R&BD Program (Project number: CR070019)
Corresponding author

Greedy Perimeter Coordinator Routing algorithm
(GPCR) [5] is an enhancement of the GPSR protocol.
It is also based on the fact that streets and junctions
naturally form a planar graph and thus does not
require any planarization algorithm. Moreover, GPCR
does not need an urban map. An important point is
that, since junctions are the only places where routing
decisions are made, packet must always be sent to a
node that is at a junction. Forwarding a packet across
a junction risks to bring GPCR to a local maximum.
At junctions, a greedy decision is also made, and the
neighboring node which brings the maximum progress
towards the destination is chosen. If a local maximum
is reached, the recovery mode is used.
And in case GpsrJ+ [6], Unlike GPCR, GpsrJ+ only
forwards packets to nodes in road junctions if and only
if the forwarding decision changes with respect to the
general forwarding direction of the recovery mode.
Otherwise, packets are allowed to progress across the
intersection with the maximum progress, saving the
protocol many hops.

The movements of vehicles are generated manually
using the Vehicle Movement Editor.

Fig. 1. Road map

Among all vehicles, 6 of them are randomly chosen
to send CBR data packet to other vehicle during the
move. To evaluate of performance each routing
protocol, we are measured by the throughput of
sending packet, data sending delay and data traffic
overhead. The simulation results are presented in
Figures 3, 4 and 5.

3. Performance evaluation in realistic
urban environment
The vehicle networks on the urban roads have many
important factors that influence, such as street layouts
and intersections with traffic signs, or inter-vehicle
interactions. But the widely used Random-Way point
Model assumes that the nodes moving in an open field
ignores such factors and without obstructions. Thus it
is important to use a realistic mobility model, so that
results from the simulation correctly reflect the real
world performance of a VANET
In our experiments, we use version 2.32 of the ns-2
simulator with the MOVE (MObility model generator
for VEhicular networks) to rapidly generate realistic
mobility models for VANET simulations [7].
First, we compared the performance of the GPSR
(position based routing) and the OLSR (reactive
routing) popular routing protocol. The street layout is
derived and normalized from a snapshot of a real
street map in the Houston area based on Topologically
Integrated Geographic Encoding and Referencing
(TIGER) database [8] form U.S. Census Bureau.
These map data are transformed into the data format
that can be used by ns2, based on techniques presented
in [9]. In our simulation, around 39 vehicles are
involved in the simulation with more than 3 â&#x20AC;&#x2122; 000
recorded vehicles position changes in an area of
around 2164m x 2195m.(Fig. 1)

Fig. 3. Throughput of sending packet

Fig. 4. Average End to End delay

Figure 3 shows the change of throughput of sending
packet. GPSR protocol is throughput of sending packet
bigger than OLSR protocol. It is because GPSR
periodically update their neighbor and sink node table
about geographical information. But Average End to
End delay of GPSR protocol is much more reduced
than OLSR protocol. (Fig. 4)

Fig. 5. The number of packets generated

Figure 5 shows the generated packet overhead as a
function of the data generated event time. As the
generated event time increases, the number of packets
generated by all protocol also increases. However, the
increasing trend is different. The overhead of GPSR
routing increases much faster than OLSR protocols
due to the redundant packets generated.

4. Routing Design of Intersection area
In real urban road environment, there are many
road intersections. To communicate with other
vehicles, in many case data packet passes intersection
area. The located of intersection affect the throughput
of sending packet and data traffic overhead. Because
at intersection passed many vehicle which change
rapidly.

Fig. 6. Intersection Area in Urban Environment

Considering the road situation on Figure 6,
Source’s vehicle (S) wants to send a packet to
Destination’s vehicle (D). If using GPSR algorithm,
sends the packet to Section 1 direction. And packet
traversing

the

cycle

is

(S→section

1→section

3→section 4→section 5→D). But it is not a optimal
path for packet delivery. So we design routing protocol
using control node, like light controller, in urban area
which has many intersection.
Our routing protocol can have of a combination of
both control node at intersection and mobile nodes on
the road. The Control nodes tend to have a stabilizing
influence on topology and routing by relaying the
packets to/from the neighbor nodes and location of last
control node for data forwarding to destination's node.
On the other hand, mobile nodes add entropy to the
system by causing frequent route setups, teardowns,
and packet losses. We assume that every mobile node
equipped with preloaded digital map and they know
its location by through navigation system. And every
control node, like light controller, are connected by
wired network and they can find their neighboring
mobile nodes through beacon messages using short
range wireless channel. And control nodes
communicated with each other about their neighbor
mobile nodes information (node id, geographical
location) and its geographical location.
We list the major steps of route path finding
algorithm at each intersections in the area. First,
source node (S) searches near two control node and
send routing path request data to control node (D).
Second, if each control nodes receives the request,
they choose control node which nearest from
destination. And they calculate fastest routing path
through their neighbor control node. And then, third
each control node sent data about their calculated path
to source node. Last, source node received reply, it
compares reply, which suggests more optimal routing
path, and forwards data to control node. Figure 7
shows the pseudo-code for the find optimal
intersection algorithm to destination node. This
algorithm is used when control node received routing
path request from source node.

5. Conclusion
GPSR is very well suited for highly dynamic
environments such as inter-vehicle communication on
highways. It has been discussed that radio obstacles,
as they are found in urban areas, have a significant
negative impact on the performance of position based
routing. To express out point, we evaluate the
performance of OLSR and GPSR with realistic urban
scenarios using MOVE(MObility model generator for
VEhicular networks).
And we conclude GPSR
protocol is throughput of sending packet bigger than
OLSR protocol. It is because GPSR periodically
update their neighbor and sink node table about
geographical information.
In addition, we presented a new position-based
routing approach which is able to deal with the
challenges of urban road environments. In real urban
road environment, there are many road intersections.
Our routing protocol can have a combination of both
control node at intersection, like light controller and
mobile nodes on the road.